R Notebook
Analysing Heart Rate and Respiration data
The goal of this assignment is to first familiarize you with heart rate, and respiration data and their preprocessing. The second part explores how to analyze interpersonal coordination of these signals.
These are the questions you need to be able to answer at the end of the assignment (aka that you need to submit as part of the portfolio)
How do you preprocess heart rate and respiration data? Describe the process. If any data needs to be excluded, list the excluded data and motivate the exclusion.
Do you observe interpersonal coordination in heart rate and respiration? Describe your control baseline, the method used to quantify coordination, and the statistical models used to infer whether coordination was higher than in the baseline. Report the results of the models.
Do you observe differences in coordination between conditions? Report the models and results.
Is respiration coordination a likely driver of heart rate coordination? Describe how you would test for it. Bonus points if you actually run the tests and report methods and results.
N.B. to give you a bit more data I included data from last year (Study1) and from your class (Study2). Note that synchronouns and turn-taking are the same across both studies, but the third condition is different: last year it was self-paced joint reading; this year it was the tv-series conversation. So you might want to exclude the self-paced reading (but, up to you!)
Step by step suggestions to solve the assignment
Exploring physiological signals
- Choose one pair (one pair, three conditions)
- Load the logs
- Produce a plot of the participants’ respiration signal and a different one of the participants’ HR signal (for inspecting whether the data is usable) N.B: remember the slides: artifacts, downsampling, scaling. N.B. The gridExtra::grid.arrange() function allows you to display the plots side by side. E.g. grid.arrange(plot1, plot2, plot3, ncol=3)
Can you eye-ball which condition if any displays more physiological coordination?
- Run crqa on heart rate and respiration data (find parameters, run crqa)
Does this tell you more than just eyeballing the plots?
# Set working directory
setwd("~/OneDrive - Aarhus universitet/AU-Cognitive Science/3rd Semester/Experimental Methods 3/Exercise/Assignments/Assignment_3NEW")
# Load relevant packages
library(ggplot2); library(groupdata2); library(dplyr); library(crqa); library(stringr); library(readr)pakke ‘ggplot2’ blev bygget under R version 3.3.2pakke ‘groupdata2’ blev bygget under R version 3.3.2pakke ‘dplyr’ blev bygget under R version 3.3.2
Vedhæfter pakke: ‘dplyr’
De følgende objekter er maskerede fra ‘package:stats’:
filter, lag
De følgende objekter er maskerede fra ‘package:base’:
intersect, setdiff, setequal, union
Indlæser krævet pakke: Matrix
pakke ‘Matrix’ blev bygget under R version 3.3.2Indlæser krævet pakke: tseriesChaos
Indlæser krævet pakke: deSolve
pakke ‘deSolve’ blev bygget under R version 3.3.2Indlæser krævet pakke: fields
pakke ‘fields’ blev bygget under R version 3.3.2Indlæser krævet pakke: spam
pakke ‘spam’ blev bygget under R version 3.3.2Indlæser krævet pakke: dotCall64
Indlæser krævet pakke: grid
kunne ikke tildele RegisteredNativeSymbol for toeplitz til toeplitz da toeplitz allerede er defineret i navnerummet ‘spam’Spam version 2.1-1 (2017-07-02) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.
Vedhæfter pakke: ‘spam’
De følgende objekter er maskerede fra ‘package:base’:
backsolve, forwardsolve
Indlæser krævet pakke: maps
pakke ‘maps’ blev bygget under R version 3.3.2Indlæser krævet pakke: plot3D
pakke ‘plot3D’ blev bygget under R version 3.3.2Indlæser krævet pakke: pracma
pakke ‘pracma’ blev bygget under R version 3.3.2
Vedhæfter pakke: ‘pracma’
Det følgende objekt er maskeret fra ‘package:deSolve’:
rk4
De følgende objekter er maskerede fra ‘package:Matrix’:
expm, lu, tril, triu
pakke ‘stringr’ blev bygget under R version 3.3.2pakke ‘readr’ blev bygget under R version 3.3.2# Load in datafile
Synch_data=read.csv("Data/Study2_G5_T1_Synchronous.csv")
Turn_data=read.csv("Data/Study2_G5_T2_TurnTaking.csv")
Conv_data=read.csv("Data/Study2_G5_T3_Conversation.csv")
# To create plots for respiration
ggplot(Conv_data, aes(x=time, y= Resp1)) + geom_line(color = "blue") + geom_line(aes(x=time, y=Resp2, color ="red")) + theme(legend.position="none")
ggplot(Turn_data, aes(x=time, y= Resp1)) + geom_line(color = "blue") + geom_line(aes(x=time, y=Resp2, color ="red")) + theme(legend.position="none")
ggplot(Synch_data, aes(x=time, y= Resp1)) + geom_line(color = "blue") + geom_line(aes(x=time, y=Resp2, color ="red")) + theme(legend.position="none")
# To create plots for heart rate
ggplot(Conv_data, aes(x=time, y= HR1)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2), color = "chartreuse4")
ggplot(Turn_data, aes(x=time, y= HR1)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2), color = "chartreuse4")
ggplot(Synch_data, aes(x=time, y= HR1)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2), color = "chartreuse4")
# To downsample data
Conv_data = Conv_data %>% group(n = 100, method = 'greedy') %>% dplyr::summarise(time = mean(time,na.rm=T), HR1 = mean(HR1,na.rm=T), HR2 = mean(HR2,na.rm=T), Resp1 = mean(Resp1,na.rm=T), Resp2 = mean(Resp2,na.rm=T))package ‘bindrcpp’ was built under R version 3.3.2Synch_data = Synch_data %>% group(n = 100, method = 'greedy') %>% dplyr::summarise(time = mean(time,na.rm=T), HR1 = mean(HR1,na.rm=T), HR2 = mean(HR2,na.rm=T), Resp1 = mean(Resp1,na.rm=T), Resp2 = mean(Resp2,na.rm=T))
Turn_data = Turn_data %>% group(n = 100, method = 'greedy') %>% dplyr::summarise(time = mean(time,na.rm=T), HR1 = mean(HR1,na.rm=T), HR2 = mean(HR2,na.rm=T), Resp1 = mean(Resp1,na.rm=T), Resp2 = mean(Resp2,na.rm=T))
# To remove outliers
removeOuts <- function(ts,threshold){
ts[ts > (mean(ts,na.rm=T) + (threshold*sd(ts,na.rm=T))) | ts < (mean(ts,na.rm=T) - (threshold*sd(ts,na.rm=T)))] = mean(ts,na.rm=T)
return(ts)}
threshold=2.5
Conv_data$HR1=removeOuts(Conv_data$HR1,threshold)
Conv_data$HR2=removeOuts(Conv_data$HR2,threshold)
Turn_data$HR1=removeOuts(Turn_data$HR1, threshold)
Turn_data$HR2=removeOuts(Turn_data$HR2, threshold)
Synch_data$HR1=removeOuts(Synch_data$HR1, threshold)
Synch_data$HR2=removeOuts(Synch_data$HR2, threshold)
Conv_data$Resp1=removeOuts(Conv_data$Resp1,threshold)
Conv_data$Resp2=removeOuts(Conv_data$Resp2,threshold)
Turn_data$Resp1=removeOuts(Turn_data$Resp1, threshold)
Turn_data$Resp2=removeOuts(Turn_data$Resp2, threshold)
Synch_data$Resp1=removeOuts(Synch_data$Resp1, threshold)
Synch_data$Resp2=removeOuts(Synch_data$Resp2, threshold)
# To scale data
Conv_data$Resp1S=scale(Conv_data$Resp1)
Conv_data$Resp2S=scale(Conv_data$Resp2)
Conv_data$HR1S=scale(Conv_data$HR1)
Conv_data$HR2S=scale(Conv_data$HR2)
Turn_data$Resp1S=scale(Turn_data$Resp1)
Turn_data$Resp2S=scale(Turn_data$Resp2)
Turn_data$HR1S=scale(Turn_data$HR1)
Turn_data$HR2S=scale(Turn_data$HR2)
Synch_data$Resp1S=scale(Synch_data$Resp1)
Synch_data$Resp2S=scale(Synch_data$Resp2)
Synch_data$HR1S=scale(Synch_data$HR1)
Synch_data$HR2S=scale(Synch_data$HR2)
# To create HR plot for sclaed data
plot1 = ggplot(Conv_data, aes(x=time, y= HR1S)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2S), color = "chartreuse4")
plot2 = ggplot(Turn_data, aes(x=time, y= HR1S)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2S), color = "chartreuse4")
plot3 = ggplot(Synch_data, aes(x=time, y= HR1S)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2S), color = "chartreuse4")
# To make one grid with all HR plots
gridExtra::grid.arrange(plot1, plot2, plot3)
plot4 = ggplot(Conv_data, aes(x=time, y= Resp1S)) + geom_line(color = "red") + geom_line(aes(x=time, y=Resp2S), color = "blue")
plot5 = ggplot(Turn_data, aes(x=time, y= Resp1S)) + geom_line(color = "red") + geom_line(aes(x=time, y=Resp2S), color = "blue")
plot6 = ggplot(Synch_data, aes(x=time, y= Resp1S)) + geom_line(color = "red") + geom_line(aes(x=time, y=Resp2S), color = "blue")
# To make one grid with all Respiration plots
gridExtra::grid.arrange(plot4, plot5, plot6)
# To perform CRQA
par = list(lgM = 50, steps = seq(1, 6, 1), radiusspan = 100, radiussample = 40, normalize = 0, rescale = 0, mindiagline = 2, minvertline = 2, tw = 0, whiteline = FALSE, recpt = FALSE, fnnpercent = 10, typeami = "mindip")
# To create a loop to get paramters
files = list(Conv_data, Synch_data, Turn_data)
Dimension_Resp = NULL
Radius_Resp = NULL
Delay_Resp = NULL
Dimension_HR = NULL
Radius_HR = NULL
Delay_HR = NULL
n=1
for (file in files) {
ans_Resp = try(optimizeParam(file$Resp1S, file$Resp2S, par, min.rec = 2, max.rec = 8))
ans_HR = try(optimizeParam(file$HR1S, file$HR2S, par, min.rec = 2, max.rec = 8))
if (length(ans_Resp) > 1) {
Dimension_Resp[n] = ans_Resp$emddim
Radius_Resp[n] = ans_Resp$radius
Delay_Resp[n] = ans_Resp$delay
}
else {
Dimension_Resp[n] = NA
Radius_Resp[n] = NA
Delay_Resp[n] = NA
}
if (length(ans_HR) > 1) {
Dimension_HR[n] = ans_HR$emddim
Radius_HR[n] = ans_HR$radius
Delay_HR[n] = ans_HR$delay
}
else {
Dimension_HR[n] = NA
Radius_HR[n] = NA
Delay_HR[n] = NA
}
n=n+1
}Optimal Radius Not found: try again choosing a wider radius span and larger sample sizeOptimal Radius Not found: try again choosing a wider radius span and larger sample sizeOptimal Radius Not found: try again choosing a wider radius span and larger sample sizeOptimal Radius Not found: try again choosing a wider radius span and larger sample sizeparameters = data.frame(Dimension_Resp, Radius_Resp, Delay_Resp, Dimension_HR, Radius_HR, Delay_HR)
mean(parameters$Dimension_Resp, na.rm = TRUE) # 2[1] 2mean(parameters$Delay_Resp, na.rm = TRUE) # 28[1] 28mean(parameters$Radius_Resp, na.rm = TRUE) # 0.378[1] 0.3778026Results=crqa (Synch_data$Resp1S, Synch_data$Resp2S, delay=28, embed=2, radius= 0.378,normalize=0,rescale=0,mindiagline = 2,minvertline = 2)
# To plot RQA
RP=Results$RP
RP = matrix(as.numeric(RP), nrow = ncol(RP))
cols = c("white","blue4")
image(RP, xlab = "", ylab = "", col = cols)
# To explore lags of coordination
Profile=drpdfromts(Synch_data$Resp1S, Synch_data$Resp2S,datatype = 'continuous',ws=50,radius=0.443)
timecourse = round( seq(-5000,5000,100)/1000, digit = 1)
maxlag = Profile$maxlag/1000
profile = Profile$profile*100
Prof=data.frame(profile)
ggplot(Prof, aes(timecourse,profile))+geom_line()+ geom_vline(xintercept = timecourse[maxlag], colour='red')
Systematically pre-process the data
- Loop through all the files (either with a loop or with a function), check which files should be excluded, if any, and save the pre-processed time-series. Tip: plot and visually inspect the data to figure out which should be excluded.
- Run crqa on all the pre-processed time-series and save the output (don’t forget to add columns with study, group, condition and trial). Tip: remember to first assess optimal parameters (dimensions, delay, radius) across all timeseries. Tip: it will often fail, just take whatever parameters you get, select optimal across timeseries parameters and run crqa on all timeseries with those. Tip: double check the rr. When I ran the loop, I got very low rr, so I adjusted the radius until the average of rr across all pairs was approx. 4%.
# To create a filelist
filelist = list.files(path = "Data", pattern = ".csv")
# To create a function that can preprocess the data
preprocessing = function(data) {
data = data %>% group(n = 100, method = 'greedy') %>% dplyr::summarise(time = mean(time,na.rm=T), HR1 = mean(HR1,na.rm=T), HR2 = mean(HR2,na.rm=T), Resp1 = mean(Resp1,na.rm=T), Resp2 = mean(Resp2,na.rm=T))
data$HR1=removeOuts(data$HR1,threshold)
data$HR2=removeOuts(data$HR2,threshold)
data$Resp1=removeOuts(data$Resp1,threshold)
data$Resp2=removeOuts(data$Resp2,threshold)
data$Resp1=scale(data$Resp1)
data$Resp2=scale(data$Resp2)
data$HR1=scale(data$HR1)
data$HR2=scale(data$HR2)
return(data)
}
# To create a loop to preproces all the data and create plots
Final_data = data.frame()
n=1
for (file in filelist) {
data=read_csv(paste0("Data/",file))
Datafile=preprocessing(data)
Datafile$filename = filelist[n]
ans_Resp = try(optimizeParam(Datafile$Resp1, Datafile$Resp2, par, min.rec = 2, max.rec = 8))
ans_HR = try(optimizeParam(Datafile$HR1, Datafile$HR2, par, min.rec = 2, max.rec = 8))
if (length(ans_Resp) > 1) {
Datafile$Dimension_Resp = ans_Resp$emddim
Datafile$Radius_Resp = ans_Resp$radius
Datafile$Delay_Resp = ans_Resp$delay
}
else {
Datafile$Dimension_Resp = NA
Datafile$Radius_Resp = NA
Datafile$Delay_Resp = NA
}
if (length(ans_HR) > 1) {
Datafile$Dimension_HR = ans_HR$emddim
Datafile$Radius_HR = ans_HR$radius
Datafile$Delay_HR = ans_HR$delay
}
else {
Datafile$Dimension_HR = NA
Datafile$Radius_HR = NA
Datafile$Delay_HR = NA
}
Final_data = rbind(Final_data, Datafile)
Resp_plot= ggplot(Datafile, aes(x=time, y= Resp1)) + geom_line(color = "red") + geom_line(aes(x=time, y=Resp2), color = "blue")
HR_plot = ggplot(Datafile, aes(x=time, y= HR1)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2), color = "chartreuse4")
final_plot=gridExtra::grid.arrange(Resp_plot, HR_plot)
ggsave(paste0(file, ".png"), plot = final_plot, path = "Plot")
n=n+1
} Parsed with column specification:
cols(
time = col_double(),
Resp1 = col_integer(),
Resp2 = col_double(),
ECG1 = col_double(),
ECG2 = col_double(),
HR1 = col_double(),
HR2 = col_double()
)
number of columns of result is not a multiple of vector length (arg 1)31102 parsing failures.
row # A tibble: 5 x 5 col row col expected actual file expected <int> <chr> <chr> <chr> <chr> actual 1 8110 Resp1 no trailing characters .99939 'Data/Study1_G1_T1_Synchronous.csv' file 2 8112 Resp1 no trailing characters .99939 'Data/Study1_G1_T1_Synchronous.csv' row 3 8113 Resp1 no trailing characters .99512 'Data/Study1_G1_T1_Synchronous.csv' col 4 8114 Resp1 no trailing characters .99207 'Data/Study1_G1_T1_Synchronous.csv' expected 5 8115 Resp1 no trailing characters .99237 'Data/Study1_G1_T1_Synchronous.csv'
... ................. ... ............................................................................... ........ ............................................................................... ...... ............................................................................... .... ............................................................................... ... ............................................................................... ... ............................................................................... ........ ...............................................................................
See problems(...) for more details.
Error : NA/NaN/Inf in foreign function call (arg 1)
Error : NA/NaN/Inf in foreign function call (arg 1)
Error : NA/NaN/Inf in foreign function call (arg 1)
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# Based on plots we choose to remove the following data:
# Study1_G1_T1, Study1_G1_T2, Study1_G2_T1, Study1_G2_T2
Final_data=Final_data[Final_data$filename != "Study1_G1_T1_Synchronous.csv", ]
Final_data=Final_data[Final_data$filename != "Study1_G1_T2_TurnTaking.csv", ]
Final_data=Final_data[Final_data$filename != "Study1_G2_T1_TurnTaking.csv", ]
Final_data=Final_data[Final_data$filename != "Study1_G2_T2_Synchronous.csv", ]
# To get column with study number
for (file in 1:nrow(Final_data)){
if (grepl("Study1", Final_data$filename[file])){
Final_data$Study[file] = 1}
if (grepl("Study2", Final_data$filename[file])){
Final_data$Study[file] = 2}
}Unknown or uninitialised column: 'Study'.# To get group number
Final_data$Group=regmatches(Final_data$filename, regexpr("[G].*[0-9]", Final_data$filename))
Final_data$Group = gsub("[G, _, T]", "", Final_data$Group)
Final_data$Group=substr(Final_data$Group, 1, nchar(Final_data$Group)-1)
# To get trial number
Final_data$Trial=regmatches(Final_data$filename, regexpr("[T].*[0-9]", Final_data$filename))
Final_data$Trial = gsub("[T]", "", Final_data$Trial)
# To get condition
Final_data = Final_data %>% group_by(filename) %>% mutate(Condition = gsub('.{4}$', '', strsplit(filename, "_")[[1]][4]))
# To write data to a csv file
write.csv(Final_data, file = "Final_data.csv")
# To get mean parameters for CRQA
mean(Final_data$Dimension_Resp, na.rm = TRUE) # 3.20 = 3[1] 3.197319mean(Final_data$Radius_Resp, na.rm = TRUE) # 0.614[1] 0.6142882mean(Final_data$Delay_Resp, na.rm = TRUE) # 29.46 = 29[1] 29.46358mean(Final_data$Dimension_HR, na.rm = TRUE) # 11.95 = 12 [1] 11.94974mean(Final_data$Radius_HR, na.rm = TRUE) # 1.633[1] 1.633311mean(Final_data$Delay_HR, na.rm = TRUE) # 29.89 = 30[1] 29.88559# To create a function that can perform CRQA for each file
CRQA = function(data) {
RespCRQA = crqa(data$Resp1, data$Resp2, delay = 29, embed = 3,
radius = 0.8, normalize = 0, rescale = 0,
mindiagline = 2, minvertline = 2)
RespCRQA$RP = NULL # Remove large matrix
RespCRQA = as_tibble(RespCRQA) %>%
mutate(type = 'Resp')
HrCRQA = crqa(data$HR1, data$HR2, delay = 30, embed = 12,
radius = 2.6, normalize = 0, rescale = 0,
mindiagline = 2, minvertline = 2)
HrCRQA$RP = NULL # Remove large matrix
HrCRQA = as_tibble(HrCRQA) %>%
mutate(type = 'HR')
return(rbind(RespCRQA, HrCRQA))
}
# To use the function
crqa_results = Final_data %>%
dplyr::select(-c(.groups)) %>% # Remove .groups (not needed right?)
dplyr::group_by(filename) %>% # Group by each file
dplyr::do(CRQA(.)) %>% # Do CRQA on each group (file)
dplyr::ungroup() # Ungroup (prob. not needed, didn't check)
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|============================================================================|100% ~0 s remaining Final_data2 = merge(Final_data, crqa_results, by = "filename")
# To write the data to a csv file
write.csv(Final_data2, file = "Final_data2.csv")Creating controls: shuffled controls
- loop through all pairs and conditions
- shuffle the timeseries (take a timeseries and rearrange its values in a random order). Tip check the sample() function
- run crqa and save the output. NB. which delay, embed, radius parameters should you use?
- statistically compare the crqa indexes in real and shuffled pairs
# To create a file list
filelist2 = list.files(path = "PreprocessedData", pattern = ".csv")
# To loop through each file, shuffle the data and perform CRQA on the shuffled data
CRQA_shuffled = data.frame()
n=1
for (file in filelist2) {
d=read_csv(paste0("PreprocessedData/",file))
d$HR1 = sample(d$HR1)
d$HR2 = sample(d$HR2)
d$Resp1 = sample(d$Resp1)
d$Resp2 = sample(d$Resp2)
data=CRQA(d)
data$filename = filelist2[n]
CRQA_shuffled=rbind(data, CRQA_shuffled)
n=n+1
}Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_integer(),
Radius_HR = col_double(),
Delay_HR = col_integer()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_integer(),
Radius_HR = col_double(),
Delay_HR = col_integer()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_integer(),
Radius_HR = col_double(),
Delay_HR = col_integer()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_integer(),
Radius_HR = col_double(),
Delay_HR = col_integer()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_character(),
Radius_Resp = col_character(),
Delay_Resp = col_character(),
Dimension_HR = col_character(),
Radius_HR = col_character(),
Delay_HR = col_character()
)
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
X1 = col_integer(),
.groups = col_integer(),
time = col_double(),
HR1 = col_double(),
HR2 = col_double(),
Resp1 = col_double(),
Resp2 = col_double(),
filename = col_character(),
Dimension_Resp = col_integer(),
Radius_Resp = col_double(),
Delay_Resp = col_integer(),
Dimension_HR = col_integer(),
Radius_HR = col_double(),
Delay_HR = col_integer()
)# To remove last 4 characters in filename (replication of .csv)
CRQA_shuffled$filename = substr(CRQA_shuffled$filename,1,nchar(CRQA_shuffled$filename)-4)
# To specify type of CRQA
crqa_results$CRQA = "Normal"
CRQA_shuffled$CRQA = "Shuffled Control"
# To combine dataset
CRQA_data = rbind(crqa_results, CRQA_shuffled)
CRQA_Resp=subset(CRQA_data[CRQA_data$type == "Resp",])
CRQA_HR=subset(CRQA_data[CRQA_data$type == "HR",])
# Statistically compare relevant CRQA indexes in normal data and shuffled controls
m1=lm(L ~ CRQA, CRQA_HR)
summary(m1)
Call:
lm(formula = L ~ CRQA, data = CRQA_HR)
Residuals:
Min 1Q Median 3Q Max
-3.7342 -0.7254 -0.0071 0.0160 12.3456
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.9489 0.4933 14.088 < 2e-16 ***
CRQAShuffled Control -4.9362 0.6976 -7.076 4.56e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.515 on 50 degrees of freedom
Multiple R-squared: 0.5004, Adjusted R-squared: 0.4904
F-statistic: 50.07 on 1 and 50 DF, p-value: 4.562e-09m2 = lm(L~ CRQA, CRQA_Resp)
summary(m2)
Call:
lm(formula = L ~ CRQA, data = CRQA_Resp)
Residuals:
Min 1Q Median 3Q Max
-0.61101 -0.01296 -0.00243 0.02301 0.93517
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.00521 0.06078 65.90 <2e-16 ***
CRQAShuffled Control -1.95728 0.08595 -22.77 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3099 on 50 degrees of freedom
Multiple R-squared: 0.9121, Adjusted R-squared: 0.9103
F-statistic: 518.6 on 1 and 50 DF, p-value: < 2.2e-16m3=lm(RR ~ CRQA, CRQA_HR)
summary(m3)
Call:
lm(formula = RR ~ CRQA, data = CRQA_HR)
Residuals:
Min 1Q Median 3Q Max
-3.4174 -1.4206 -0.5285 0.8121 9.9431
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.8194 0.5205 7.338 1.78e-09 ***
CRQAShuffled Control -2.3049 0.7361 -3.131 0.00291 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.654 on 50 degrees of freedom
Multiple R-squared: 0.1639, Adjusted R-squared: 0.1472
F-statistic: 9.804 on 1 and 50 DF, p-value: 0.002907m4 = lm(RR~ CRQA, CRQA_Resp)
summary(m4)
Call:
lm(formula = RR ~ CRQA, data = CRQA_Resp)
Residuals:
Min 1Q Median 3Q Max
-1.7477 -0.5310 -0.1684 0.4924 2.2263
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.9838 0.1582 31.51 <2e-16 ***
CRQAShuffled Control -0.4116 0.2237 -1.84 0.0717 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8065 on 50 degrees of freedom
Multiple R-squared: 0.06343, Adjusted R-squared: 0.0447
F-statistic: 3.386 on 1 and 50 DF, p-value: 0.07167m5 = lm (ENTR ~ CRQA_HR)Fejl i eval(expr, envir, enclos) : objekt 'ENTR' blev ikke fundetTRICKY! Creating controls: surrogate pair controls
- Per each real pair, identify at least one surrogate pair (matching one of the participants, with somebody doing the same task, but in a different pair). Tip: Celine will share a commented script
- Run crqa on all the surrogate pairs and save the output. NB. which delay, embed, radius parameters should you use?
- Test whether crqa shows a difference between real and surrogate pairs ### Testing effects of conditions
- make a (probably underpowered) mixed model testing effects of the different conditions on heart rate and respiration coordination
- N.B: would it make sense to include surrogate pairs? and if so how? what would that tell you?
summary(m6)
Call:
lm(formula = RR ~ CRQA, data = CRQA_Resp)
Residuals:
Min 1Q Median 3Q Max
-1.7477 -0.6804 -0.2512 0.5225 5.8528
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.98379 0.19922 25.016 <2e-16 ***
CRQAShuffled Control -0.41161 0.28174 -1.461 0.145
CRQASurrogate 0.05509 0.20524 0.268 0.788
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.016 on 473 degrees of freedom
Multiple R-squared: 0.01085, Adjusted R-squared: 0.00667
F-statistic: 2.595 on 2 and 473 DF, p-value: 0.07572Testing effects of conditions
- make a (probably underpowered) mixed model testing effects of the different conditions on heart rate and respiration coordination
- N.B: would it make sense to include surrogate pairs? and if so how? what would that tell you?
summary(m_ENTR_resp)
Linear mixed model fit by REML t-tests use Satterthwaite approximations to degrees of
freedom [lmerMod]
Formula: ENTR ~ Condition + (1 | Study) + (1 + Trial | Group)
Data: Finaldata_Resp
REML criterion at convergence: -111832.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.1216 -0.8903 0.1167 0.6473 1.7808
Random effects:
Groups Name Variance Std.Dev. Corr
Group (Intercept) 4.996e-02 0.223510537
Trial 1.793e-02 0.133884757 -0.91
Study (Intercept) 3.288e-12 0.000001813
Residual 8.553e-03 0.092484166
Number of obs: 58240, groups: Group, 10; Study, 2
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.855845 0.031530 7.000000 58.86 1.56e-10 ***
ConditionSelfPaced 0.288444 0.002866 58217.000000 100.66 < 2e-16 ***
ConditionSynchronous 0.015790 0.001514 58227.000000 10.43 < 2e-16 ***
ConditionTurnTaking -0.037968 0.001514 58229.000000 -25.09 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) CndtSP CndtnS
CndtnSlfPcd -0.025
CndtnSynchr -0.027 0.322
CndtnTrnTkn -0.029 0.499 0.555Effects of respiration coordination on heart rate coordination
- describe how you would test those.
---
title: "R Notebook"
output: html_notebook
---

## Analysing Heart Rate and Respiration data

The goal of this assignment is to first familiarize you with heart rate, and respiration data and their preprocessing. The second part explores how to analyze interpersonal coordination of these signals.

These are the questions you need to be able to answer at the end of the assignment (aka that you need to submit as part of the portfolio)

1) How do you preprocess heart rate and respiration data? Describe the process. If any data needs to be excluded, list the excluded data and motivate the exclusion.

2) Do you observe interpersonal coordination in heart rate and respiration? Describe your control baseline, the method used to quantify coordination, and the statistical models used to infer whether coordination was higher than in the baseline. Report the results of the models.

3) Do you observe differences in coordination between conditions? Report the models and results.

4) Is respiration coordination a likely driver of heart rate coordination? Describe how you would test for it. Bonus points if you actually run the tests and report methods and results.

N.B. to give you a bit more data I included data from last year (Study1) and from your class (Study2). Note that synchronouns and turn-taking are the same across both studies, but the third condition is different: last year it was self-paced joint reading; this year it was the tv-series conversation. So you might want to exclude the self-paced reading (but, up to you!)

## Step by step suggestions to solve the assignment

### Exploring physiological signals

- Choose one pair (one pair, three conditions)
- Load the logs
- Produce a plot of the participants' respiration signal and a different one of the participants' HR signal (for inspecting whether the data is usable)
  N.B: remember the slides: artifacts, downsampling, scaling.
  N.B. The gridExtra::grid.arrange() function allows you to display the plots side by side. E.g. grid.arrange(plot1, plot2, plot3, ncol=3)
- Can you eye-ball which condition if any displays more physiological coordination?

- Run crqa on heart rate and respiration data (find parameters, run crqa)
- Does this tell you more than just eyeballing the plots?
```{r}
# Set working directory
setwd("~/OneDrive - Aarhus universitet/AU-Cognitive Science/3rd Semester/Experimental Methods 3/Exercise/Assignments/Assignment_3NEW")

# Load relevant packages
library(ggplot2); library(groupdata2); library(dplyr); library(crqa); library(stringr); library(readr)

# Load in datafile
Synch_data=read.csv("Data/Study2_G5_T1_Synchronous.csv")
Turn_data=read.csv("Data/Study2_G5_T2_TurnTaking.csv")
Conv_data=read.csv("Data/Study2_G5_T3_Conversation.csv")

# To create plots for respiration 
ggplot(Conv_data, aes(x=time, y= Resp1)) + geom_line(color = "blue") + geom_line(aes(x=time, y=Resp2, color ="red")) + theme(legend.position="none")
ggplot(Turn_data, aes(x=time, y= Resp1)) + geom_line(color = "blue") + geom_line(aes(x=time, y=Resp2, color ="red")) + theme(legend.position="none")
ggplot(Synch_data, aes(x=time, y= Resp1)) + geom_line(color = "blue") + geom_line(aes(x=time, y=Resp2, color ="red")) + theme(legend.position="none")


# To create plots for heart rate
ggplot(Conv_data, aes(x=time, y= HR1)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2), color = "chartreuse4")
ggplot(Turn_data, aes(x=time, y= HR1)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2), color = "chartreuse4")
ggplot(Synch_data, aes(x=time, y= HR1)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2), color = "chartreuse4")

# To downsample data
Conv_data = Conv_data %>% group(n = 100, method = 'greedy') %>%   dplyr::summarise(time = mean(time,na.rm=T), HR1 = mean(HR1,na.rm=T), HR2 = mean(HR2,na.rm=T), Resp1 = mean(Resp1,na.rm=T), Resp2 = mean(Resp2,na.rm=T))

Synch_data = Synch_data %>% group(n = 100, method = 'greedy') %>%   dplyr::summarise(time = mean(time,na.rm=T), HR1 = mean(HR1,na.rm=T), HR2 = mean(HR2,na.rm=T), Resp1 = mean(Resp1,na.rm=T), Resp2 = mean(Resp2,na.rm=T))

Turn_data = Turn_data %>% group(n = 100, method = 'greedy') %>%   dplyr::summarise(time = mean(time,na.rm=T), HR1 = mean(HR1,na.rm=T), HR2 = mean(HR2,na.rm=T), Resp1 = mean(Resp1,na.rm=T), Resp2 = mean(Resp2,na.rm=T))

# To remove outliers
removeOuts <- function(ts,threshold){
  ts[ts > (mean(ts,na.rm=T) +              (threshold*sd(ts,na.rm=T))) |  ts < (mean(ts,na.rm=T) -           (threshold*sd(ts,na.rm=T)))] = mean(ts,na.rm=T)   
  return(ts)}

threshold=2.5 

Conv_data$HR1=removeOuts(Conv_data$HR1,threshold)
Conv_data$HR2=removeOuts(Conv_data$HR2,threshold)
Turn_data$HR1=removeOuts(Turn_data$HR1, threshold)
Turn_data$HR2=removeOuts(Turn_data$HR2, threshold)
Synch_data$HR1=removeOuts(Synch_data$HR1, threshold)
Synch_data$HR2=removeOuts(Synch_data$HR2, threshold)

Conv_data$Resp1=removeOuts(Conv_data$Resp1,threshold)
Conv_data$Resp2=removeOuts(Conv_data$Resp2,threshold)
Turn_data$Resp1=removeOuts(Turn_data$Resp1, threshold)
Turn_data$Resp2=removeOuts(Turn_data$Resp2, threshold)
Synch_data$Resp1=removeOuts(Synch_data$Resp1, threshold)
Synch_data$Resp2=removeOuts(Synch_data$Resp2, threshold)

# To scale data
Conv_data$Resp1S=scale(Conv_data$Resp1)
Conv_data$Resp2S=scale(Conv_data$Resp2)  
Conv_data$HR1S=scale(Conv_data$HR1)
Conv_data$HR2S=scale(Conv_data$HR2)

Turn_data$Resp1S=scale(Turn_data$Resp1)
Turn_data$Resp2S=scale(Turn_data$Resp2)  
Turn_data$HR1S=scale(Turn_data$HR1)
Turn_data$HR2S=scale(Turn_data$HR2)

Synch_data$Resp1S=scale(Synch_data$Resp1)
Synch_data$Resp2S=scale(Synch_data$Resp2)  
Synch_data$HR1S=scale(Synch_data$HR1)
Synch_data$HR2S=scale(Synch_data$HR2)

# To create HR plot for sclaed data
plot1 = ggplot(Conv_data, aes(x=time, y= HR1S)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2S), color = "chartreuse4")
plot2 = ggplot(Turn_data, aes(x=time, y= HR1S)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2S), color = "chartreuse4")
plot3 = ggplot(Synch_data, aes(x=time, y= HR1S)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2S), color = "chartreuse4")

# To make one grid with all HR plots
gridExtra::grid.arrange(plot1, plot2, plot3)

plot4 = ggplot(Conv_data, aes(x=time, y= Resp1S)) + geom_line(color = "red") + geom_line(aes(x=time, y=Resp2S), color = "blue")
plot5 = ggplot(Turn_data, aes(x=time, y= Resp1S)) + geom_line(color = "red") + geom_line(aes(x=time, y=Resp2S), color = "blue")
plot6 = ggplot(Synch_data, aes(x=time, y= Resp1S)) + geom_line(color = "red") + geom_line(aes(x=time, y=Resp2S), color = "blue")

# To make one grid with all Respiration plots
gridExtra::grid.arrange(plot4, plot5, plot6)

# To perform CRQA
par = list(lgM =  50, steps = seq(1, 6, 1),  radiusspan = 100,  radiussample = 40, normalize = 0,  rescale = 0,  mindiagline = 2,  minvertline = 2,  tw = 0,  whiteline = FALSE,  recpt = FALSE,  fnnpercent = 10,  typeami = "mindip")

# To create a loop to get paramters 
files = list(Conv_data, Synch_data, Turn_data)

Dimension_Resp = NULL
Radius_Resp = NULL
Delay_Resp = NULL
Dimension_HR = NULL
Radius_HR = NULL
Delay_HR = NULL

n=1

for (file in files) {
ans_Resp = try(optimizeParam(file$Resp1S, file$Resp2S, par, min.rec = 2, max.rec = 8))
ans_HR = try(optimizeParam(file$HR1S, file$HR2S, par, min.rec = 2, max.rec = 8))

if (length(ans_Resp) > 1) {
  
Dimension_Resp[n] = ans_Resp$emddim
Radius_Resp[n] = ans_Resp$radius
Delay_Resp[n] = ans_Resp$delay
  } 
  
  else {
    
Dimension_Resp[n] = NA
Radius_Resp[n] = NA
Delay_Resp[n] = NA
    
  }

if (length(ans_HR) > 1) {
  
Dimension_HR[n] = ans_HR$emddim
Radius_HR[n] = ans_HR$radius
Delay_HR[n] = ans_HR$delay
  } 
  
  else {
    
Dimension_HR[n] = NA
Radius_HR[n] = NA
Delay_HR[n] = NA
    
  }
  
n=n+1

}

parameters = data.frame(Dimension_Resp, Radius_Resp, Delay_Resp, Dimension_HR, Radius_HR, Delay_HR)


mean(parameters$Dimension_Resp, na.rm = TRUE) # 2
mean(parameters$Delay_Resp, na.rm = TRUE) # 28
mean(parameters$Radius_Resp, na.rm = TRUE) # 0.378

Results=crqa (Synch_data$Resp1S, Synch_data$Resp2S, delay=28, embed=2, radius= 0.378,normalize=0,rescale=0,mindiagline = 2,minvertline = 2)

# To plot RQA
RP=Results$RP 
RP = matrix(as.numeric(RP), nrow = ncol(RP))  
cols = c("white","blue4") 
image(RP, xlab = "", ylab = "", col = cols)

# To explore lags of coordination 
Profile=drpdfromts(Synch_data$Resp1S, Synch_data$Resp2S,datatype = 'continuous',ws=50,radius=0.443) 
timecourse = round( seq(-5000,5000,100)/1000, digit = 1) 
maxlag = Profile$maxlag/1000 
profile = Profile$profile*100 
Prof=data.frame(profile) 
ggplot(Prof, aes(timecourse,profile))+geom_line()+ geom_vline(xintercept = timecourse[maxlag], colour='red')

```
 
### Systematically pre-process the data
- Loop through all the files (either with a loop or with a function), check which files should be excluded, if any, and save the pre-processed time-series. Tip: plot and visually inspect the data to figure out which should be excluded.
- Run crqa on all the pre-processed time-series and save the output (don't forget to add columns with study, group, condition and trial). Tip: remember to first assess optimal parameters (dimensions, delay, radius) across all timeseries. Tip: it will often fail, just take whatever parameters you get, select optimal across timeseries parameters and run crqa on all timeseries with those. Tip: double check the rr. When I ran the loop, I got very low rr, so I adjusted the radius until the average of rr across all pairs was approx. 4%.

```{r}
# To create a filelist
filelist = list.files(path = "Data", pattern = ".csv")

# To create a function that can preprocess the data
preprocessing = function(data) {
  
  data = data %>% group(n = 100, method = 'greedy') %>%   dplyr::summarise(time = mean(time,na.rm=T), HR1 = mean(HR1,na.rm=T), HR2 = mean(HR2,na.rm=T), Resp1 = mean(Resp1,na.rm=T), Resp2 = mean(Resp2,na.rm=T))
  
  data$HR1=removeOuts(data$HR1,threshold)
  data$HR2=removeOuts(data$HR2,threshold)
  data$Resp1=removeOuts(data$Resp1,threshold)
  data$Resp2=removeOuts(data$Resp2,threshold)
  
  data$Resp1=scale(data$Resp1)
  data$Resp2=scale(data$Resp2)  
  data$HR1=scale(data$HR1)
  data$HR2=scale(data$HR2)
  
  return(data)
}

# To create a loop to preproces all the data and create plots
Final_data = data.frame()

n=1

for (file in filelist) {
  data=read_csv(paste0("Data/",file))
  
  Datafile=preprocessing(data)
  
  Datafile$filename = filelist[n]
  
  ans_Resp = try(optimizeParam(Datafile$Resp1, Datafile$Resp2, par, min.rec = 2, max.rec = 8))
  ans_HR = try(optimizeParam(Datafile$HR1, Datafile$HR2, par, min.rec = 2, max.rec = 8))
  
  if (length(ans_Resp) > 1) {
  
Datafile$Dimension_Resp = ans_Resp$emddim
Datafile$Radius_Resp = ans_Resp$radius
Datafile$Delay_Resp = ans_Resp$delay
  } 
  
  else {
    
Datafile$Dimension_Resp = NA
Datafile$Radius_Resp = NA
Datafile$Delay_Resp = NA
    
  }

if (length(ans_HR) > 1) {
  
Datafile$Dimension_HR = ans_HR$emddim
Datafile$Radius_HR = ans_HR$radius
Datafile$Delay_HR = ans_HR$delay
  } 
  
  else {
    
Datafile$Dimension_HR = NA
Datafile$Radius_HR = NA
Datafile$Delay_HR = NA
    
  }
  
  Final_data = rbind(Final_data, Datafile)
  
  Resp_plot= ggplot(Datafile, aes(x=time, y= Resp1)) + geom_line(color = "red") + geom_line(aes(x=time, y=Resp2), color = "blue")
  
  HR_plot = ggplot(Datafile, aes(x=time, y= HR1)) + geom_line(color = "purple") + geom_line(aes(x=time, y=HR2), color = "chartreuse4")
  
  final_plot=gridExtra::grid.arrange(Resp_plot, HR_plot)
  
  ggsave(paste0(file, ".png"), plot = final_plot, path = "Plot")
  
  n=n+1
} 

# Based on plots we choose to remove the following data:
  # Study1_G1_T1, Study1_G1_T2, Study1_G2_T1, Study1_G2_T2

Final_data=Final_data[Final_data$filename != "Study1_G1_T1_Synchronous.csv", ]
Final_data=Final_data[Final_data$filename != "Study1_G1_T2_TurnTaking.csv", ]
Final_data=Final_data[Final_data$filename != "Study1_G2_T1_TurnTaking.csv", ]
Final_data=Final_data[Final_data$filename != "Study1_G2_T2_Synchronous.csv", ]

# To get column with study number
for (file in 1:nrow(Final_data)){
  if (grepl("Study1", Final_data$filename[file])){
    Final_data$Study[file] = 1}
  if (grepl("Study2", Final_data$filename[file])){
    Final_data$Study[file] = 2}
}

# To get group number
Final_data$Group=regmatches(Final_data$filename, regexpr("[G].*[0-9]", Final_data$filename))

Final_data$Group = gsub("[G, _, T]", "", Final_data$Group)

Final_data$Group=substr(Final_data$Group, 1, nchar(Final_data$Group)-1)

# To get trial number
Final_data$Trial=regmatches(Final_data$filename, regexpr("[T].*[0-9]", Final_data$filename))

Final_data$Trial = gsub("[T]", "", Final_data$Trial)

# To get condition
Final_data = Final_data %>% group_by(filename) %>% mutate(Condition = gsub('.{4}$', '', strsplit(filename, "_")[[1]][4]))

# To write data to a csv file
write.csv(Final_data, file = "Final_data.csv")

# To get mean parameters for CRQA
mean(Final_data$Dimension_Resp, na.rm = TRUE) # 3.20 = 3
mean(Final_data$Radius_Resp, na.rm = TRUE) # 0.614
mean(Final_data$Delay_Resp, na.rm = TRUE) # 29.46 = 29

mean(Final_data$Dimension_HR, na.rm = TRUE) # 11.95 = 12 
mean(Final_data$Radius_HR, na.rm = TRUE) # 1.633
mean(Final_data$Delay_HR, na.rm = TRUE) # 29.89 = 30

# To create a function that can perform CRQA for each file
CRQA = function(data) {
  RespCRQA = crqa(data$Resp1, data$Resp2, delay = 29, embed = 3,
                   radius = 0.8, normalize = 0, rescale = 0,
                   mindiagline = 2, minvertline = 2)
  RespCRQA$RP = NULL  # Remove large matrix
  RespCRQA = as_tibble(RespCRQA) %>%
    mutate(type = 'Resp')
  
  HrCRQA = crqa(data$HR1, data$HR2, delay = 30, embed = 12,
                radius = 2.6, normalize = 0, rescale = 0,
                mindiagline = 2, minvertline = 2)
  HrCRQA$RP = NULL  # Remove large matrix
  HrCRQA = as_tibble(HrCRQA) %>%
    mutate(type = 'HR')
  
  return(rbind(RespCRQA, HrCRQA))
}

# To use the function 
crqa_results = Final_data %>%
  dplyr::select(-c(.groups)) %>%  # Remove .groups (not needed right?)
  dplyr::group_by(filename) %>%  # Group by each file
  dplyr::do(CRQA(.)) %>%  # Do CRQA on each group (file)
  dplyr::ungroup()  # Ungroup (prob. not needed, didn't check) 

Final_data2 = merge(Final_data, crqa_results, by = "filename")

# To write the data to a csv file
write.csv(Final_data2, file = "Final_data2.csv")

```

### Creating controls: shuffled controls
 - loop through all pairs and conditions
 - shuffle the timeseries (take a timeseries and rearrange its values in a random order). Tip check the sample() function
 - run crqa and save the output. NB. which delay, embed, radius parameters should you use?
 - statistically compare the crqa indexes in real and shuffled pairs
 
```{r}
# To create a file list
filelist2 = list.files(path = "PreprocessedData", pattern = ".csv")

# To loop through each file, shuffle the data and perform CRQA on the shuffled data
CRQA_shuffled = data.frame()
n=1

for (file in filelist2) {
  d=read_csv(paste0("PreprocessedData/",file))
  
  d$HR1 = sample(d$HR1)
  d$HR2 = sample(d$HR2)
  d$Resp1 = sample(d$Resp1)
  d$Resp2 = sample(d$Resp2)
  
  data=CRQA(d)
  
  data$filename = filelist2[n]
  
  CRQA_shuffled=rbind(data, CRQA_shuffled)
  
  n=n+1
  
}

# To remove last 4 characters in filename (replication of .csv)
CRQA_shuffled$filename = substr(CRQA_shuffled$filename,1,nchar(CRQA_shuffled$filename)-4)

# To specify type of CRQA
crqa_results$CRQA = "Normal"
CRQA_shuffled$CRQA = "Shuffled Control"

# To combine dataset
CRQA_data = rbind(crqa_results, CRQA_shuffled)
CRQA_Resp=subset(CRQA_data[CRQA_data$type == "Resp",])
CRQA_HR=subset(CRQA_data[CRQA_data$type == "HR",])

# Statistically compare relevant CRQA indexes in normal data and shuffled controls
m1=lm(L ~ CRQA, CRQA_HR)
summary(m1)

m2 = lm(L~ CRQA, CRQA_Resp)
summary(m2)

m3=lm(RR ~ CRQA, CRQA_HR)
summary(m3)

m4 = lm(RR~ CRQA, CRQA_Resp)
summary(m4)

m5 = lm (ENTR ~ CRQA_HR)
summary(m5)

m6 = lm(ENTR ~ CRQA_Resp)
summary(m6)
summary(m3)
```
### TRICKY! Creating controls: surrogate pair controls
 - Per each real pair, identify at least one surrogate pair (matching one of the participants, with somebody doing the same task, but in a different pair). Tip: Celine will share a commented script
 - Run crqa on all the surrogate pairs and save the output. NB. which delay, embed, radius parameters should you use?
 - Test whether crqa shows a difference between real and surrogate pairs
 ### Testing effects of conditions
 - make a (probably underpowered) mixed model testing effects of the different conditions on heart rate and respiration coordination
 - N.B: would it make sense to include surrogate pairs? and if so how? what would that tell you?
```{r}
#To create surrogate pairs for study 1
prepro_filelist = list.files(pattern = "Study1", path = "PreprocessedAll") # load the preprocessed files

num=str_extract_all(prepro_filelist, "\\d+") #extract all numbers in the file name (i.e Study?_G?_T?)

df= as.data.frame(t(as.data.frame(num))) #create a transposed (i.e. "tipped") dataframe of these number
rownames(df)<-NULL #Remove rownames.

pairs=as.numeric(unique(as.numeric(as.character(df$V2)))) #create pairs, i.e. unique group numbers

SurrogateList <- expand.grid(a = pairs, b = pairs) # expand.grid combines all possible combinations of a given vector
SurrogateList = subset(SurrogateList, a != b) # Ensure that pair 1 (i.e group1) doesn't get paired with it self.


# To set working directory
setwd("~/OneDrive - Aarhus universitet/AU-Cognitive Science/3rd Semester/Experimental Methods 3/Exercise/Assignments/Assignment_3NEW/PreprocessedAll")
# Per each condition
for (co in c("Synchronous","TurnTaking","SelfPaced")){ #loop through a list of condintions 
  for (i in 1:nrow(SurrogateList)){ #and loop through all the pairings of the surrogate list
    
  #start recreate filenames from the working directory based on the matching from the surrogatelist
  f1=paste0('Study1_G',SurrogateList$a[i]) #Insert group numeber from Surrogatelist 
  f2=paste0('Study1_G',SurrogateList$b[i])
  
  file1=prepro_filelist[grepl(f1,prepro_filelist) & grepl(co,prepro_filelist)] #insert conditions as well.
  file2=prepro_filelist[grepl(f2,prepro_filelist) & grepl(co,prepro_filelist)]
  
  d1=read.csv(file1) #Load the recreated filenames
  
  d2=read.csv(file2) 
  
  #Ensure both dataframes have the same length
  if (nrow(d1)>nrow(d2)){
    d1=d1[1:nrow(d2),]
  } else {
    d2=d2[1:nrow(d1),]
  }
  
  #creation of surrogate files. (Riccardo's idea of having fun and completely overdoing it ;-) ) 
  #per each 2 pairs there are 4 possible surrogate pairs: 
  #pair1person1 w pair2person2; 
  d3=data.frame(Resp1=d1$Resp1,Resp2=d2$Resp2,HR1=d1$HR1,HR2=d2$HR2)
  #pair1person2 w pair2person2;
  d4=data.frame(Resp1=d2$Resp1,Resp2=d1$Resp2,HR1=d2$HR1,HR2=d1$HR2)
  
  #pair1person1 w pair2person1;
  d5=data.frame(Resp1=d1$Resp1,Resp2=d2$Resp1,HR1=d1$HR1,HR2=d2$HR1)
  #pair1person2 w pair2person2
  d6=data.frame(Resp1=d2$Resp2,Resp2=d1$Resp2,HR1=d2$HR2,HR2=d1$HR2)
  
  #Make filenames that tells which pairs have been mixed
  file3=paste0("Study1_SurrogateGroup",SurrogateList$a[i],"&",SurrogateList$b[i],"a_",co,".csv")
  file4=paste0("Study1_SurrogateGroup",SurrogateList$a[i],"&",SurrogateList$b[i],"b_",co,".csv")
  file5=paste0("Study1_SurrogateGroup",SurrogateList$a[i],"&",SurrogateList$b[i],"c_",co,".csv")
  file6=paste0("Study1_SurrogateGroup",SurrogateList$a[i],"&",SurrogateList$b[i],"d_",co,".csv")
  
  #write csv files
  write_csv(d3,file.path("surrogate",file3))
  write_csv(d4,file.path("surrogate",file4))
  write_csv(d5,file.path("surrogate",file5))
  write_csv(d6,file.path("surrogate",file6))
  }
}

# To create surrogate pairs for Study 2

prepro_filelist = list.files(pattern = "Study2") # load the preprocessed files

num=str_extract_all(prepro_filelist, "\\d+") #extract all numbers in the file name (i.e Study?_G?_T?)

df= as.data.frame(t(as.data.frame(num))) #create a transposed (i.e. "tipped") dataframe of these number
rownames(df)<-NULL #Remove rownames.

pairs=as.numeric(unique(as.numeric(as.character(df$V2)))) #create pairs, i.e. unique group numbers

SurrogateList <- expand.grid(a = pairs, b = pairs) # expand.grid combines all possible combinations of a given vector
SurrogateList = subset(SurrogateList, a != b) # Ensure that pair 1 (i.e group1) doesn't get paired with it self.

# Per each condition
for (co in c("Synchronous","TurnTaking","Conversation")){ #loop through a list of condintions 
  for (i in 1:nrow(SurrogateList)){ #and loop through all the pairings of the surrogate list
    
  #start recreate filenames from the working directory based on the matching from the surrogatelist
  f1=paste0('Study2_G',SurrogateList$a[i]) #Insert group numeber from Surrogatelist 
  f2=paste0('Study2_G',SurrogateList$b[i])
  
  file1=prepro_filelist[grepl(f1,prepro_filelist) & grepl(co,prepro_filelist)] #insert conditions as well.
  file2=prepro_filelist[grepl(f2,prepro_filelist) & grepl(co,prepro_filelist)]
  
  d1=read.csv(file1) #Load the recreated filenames
  
  d2=read.csv(file2) 
  
  #Ensure both dataframes have the same length
  if (nrow(d1)>nrow(d2)){
    d1=d1[1:nrow(d2),]
  } else {
    d2=d2[1:nrow(d1),]
  }
  
  #creation of surrogate files. (Riccardo's idea of having fun and completely overdoing it ;-) ) 
  #per each 2 pairs there are 4 possible surrogate pairs: 
  #pair1person1 w pair2person2; 
  d3=data.frame(Resp1=d1$Resp1,Resp2=d2$Resp2,HR1=d1$HR1,HR2=d2$HR2)
  #pair1person2 w pair2person2;
  d4=data.frame(Resp1=d2$Resp1,Resp2=d1$Resp2,HR1=d2$HR1,HR2=d1$HR2)
  
  #pair1person1 w pair2person1;
  d5=data.frame(Resp1=d1$Resp1,Resp2=d2$Resp1,HR1=d1$HR1,HR2=d2$HR1)
  #pair1person2 w pair2person2
  d6=data.frame(Resp1=d2$Resp2,Resp2=d1$Resp2,HR1=d2$HR2,HR2=d1$HR2)
  
  #Make filenames that tells which pairs have been mixed
  file3=paste0("Study2_SurrogateGroup",SurrogateList$a[i],"&",SurrogateList$b[i],"a_",co,".csv")
  file4=paste0("Study2_SurrogateGroup",SurrogateList$a[i],"&",SurrogateList$b[i],"b_",co,".csv")
  file5=paste0("Study2_SurrogateGroup",SurrogateList$a[i],"&",SurrogateList$b[i],"c_",co,".csv")
  file6=paste0("Study2_SurrogateGroup",SurrogateList$a[i],"&",SurrogateList$b[i],"d_",co,".csv")
  
  #write csv files
  write_csv(d3,file.path("surrogate",file3))
  write_csv(d4,file.path("surrogate",file4))
  write_csv(d5,file.path("surrogate",file5))
  write_csv(d6,file.path("surrogate",file6))
  }
}

setwd("~/OneDrive - Aarhus universitet/AU-Cognitive Science/3rd Semester/Experimental Methods 3/Exercise/Assignments/Assignment_3NEW/PreprocessedAll")

# To create new filelist
filelist3 = list.files(path = "surrogate") 

# To perform CRQA on surrogate pairs
CRQA_surrogate = data.frame()
n=1

for (file in filelist3) {
  d=read_csv(paste0("surrogate/",file))
  
  data=CRQA(d)
  
  data$filename = filelist3[n]
  
  CRQA_surrogate=rbind(data, CRQA_surrogate)
  
  n=n+1
  
}

# To save the file to a csv file
write.csv(CRQA_surrogate, file = "CRQA_surrogate.csv")

# To specify type of CRQA
CRQA_surrogate$CRQA = "Surrogate"

# To combine dataset
CRQA_data = rbind(CRQA_data, CRQA_surrogate)
CRQA_Resp=subset(CRQA_data[CRQA_data$type == "Resp",])
CRQA_HR=subset(CRQA_data[CRQA_data$type == "HR",])

##HERE
# Statistically compare CRQA indexes between normal and surrogate pairs 
m1=lm(L ~ CRQA, CRQA_HR)
summary(m1)

m2 = lm(L~ CRQA, CRQA_Resp)
summary(m2)

m3=lm(ENTR ~ CRQA, CRQA_HR)
summary(m3)

m4 = lm(ENTR~ CRQA, CRQA_Resp)
summary(m4)

m5=lm(RR ~ CRQA, CRQA_HR)
summary(m5)

m6 = lm(RR ~ CRQA, CRQA_Resp)
summary(m6)


```
### Testing effects of conditions
- make a (probably underpowered) mixed model testing effects of the different conditions on heart rate and respiration coordination
- N.B: would it make sense to include surrogate pairs? and if so how? what would that tell you?
```{r}
Final_data2x=read.csv("Final_data2.csv")
# To subset data into heart rate and respiration data
Finaldata_Resp=subset(Final_data2x[Final_data2x$type == "Resp",])
Finaldata_HR=subset(Final_data2x[Final_data2x$type == "HR",])

# To statistically investigate the relationship between condition and CRQA parameters
m_RR_resp=lmer(RR ~ Condition + (1|Study) + (1+Trial|Group) , Finaldata_Resp)
summary(m_RR_resp)

m_RR_HR=lmer(RR ~ Condition + (1|Study) + (1+Trial|Group) , Finaldata_HR)
summary(m_RR_HR)

m_L_resp=lmer(L ~ Condition + (1|Study) + (1+Trial|Group) , Finaldata_Resp)
summary(m_L_resp)

m_L_HR=lmer(L ~ Condition + (1|Study) + (1+Trial|Group) , Finaldata_HR)
summary(m_L_HR)

m_ENTR_resp=lmer(ENTR ~ Condition + (1|Study) + (1+Trial|Group) , Finaldata_Resp)
summary(m_ENTR_resp)

m_ENTR_HR=lmer(ENTR ~ Condition + (1|Study) + (1+Trial|Group) , Finaldata_HR)
summary(m_ENTR_HR)

## USed code: options(scipen=3), to get fewer decimals instead of scientific notation
summary(m_RR_HR)
summary(m_L_HR)
summary(m_ENTR_HR)

summary(m_RR_resp)
summary(m_L_resp)
summary(m_ENTR_resp)

```
 
### Effects of respiration coordination on heart rate coordination
 - describe how you would test those.